Mental health research relies heavily on antiquated systems of measurement. The construction of traditional mental health scales is based largely on subjective judgment, and at best, application of methods from classical test theory to determine a scale's psychometric properties. In this application we borrow strength from major advances in test construction and administration that have been developed in the fields of educational measurement and modem psychometric theory. In particular, we propose to use Item Response Theory (IRT) to calibrate a large item pool of 626 mood disorder items and then adaptively administer them such that a given subject can be evaluated on a small subset of the items to any practical degree of accuracy. The use of the IRT model allows us to evaluate the intensity of the mood disorder for different subjects who have taken potentially different numbers of items selected from the item pool. Using computerized adaptive testing (CAT) we can then adaptively select the most appropriate set of items for each subject based on his/her responses to previous items, beginning from a small screening set of items that characterize low to high levels of impairment. The net result is that large "item banks" can be developed that thoroughly characterize a particular disorder. Although it is not routinely possible for any one subject to be evaluated on all of the items, CAT permits each subject to be evaluated on a small subset of the total item pool, with minimal and controllable loss of information. A complication of applying IRT to mental health measurement problems is that unlike traditional ability testing (e.g., mathematics achievement) which are inherently unidimensional, mental health measurement scales are inherently multidimensional. Although multidimensional IRT models are available they have not been well studied in the Context of adaptive testing. Note that one of the primary reasons for the multidimensionality of mental health measurement scales is that the items are often sampled from multiple domains (e.g., various mood disorders), thereby violating the assumption of a unidimensional IRT model. To this end, Gibbons and Hedeker (1992) developed an "item bi-factor" model which allows each item to load on a primary dimension (e.g., depression) and one subdomain (e.g., sleep disturbance). In the context of mental health measurement, the advantage of the hi-factor model is that it yields a measure of overall impairment that can be the focus of adaptive testing. In this application, we propose to provide an in depth feasibility study of the use of CAT and IRT in the calibration and administration of mental health measurement instruments. Specifically, we propose to develop the statistical theory and software that is necessary, and to then apply it to the Mood-Anxiety Spectrum Disorders Scale.